Principled, practical, flexible, fast: a new approach to phylogenetic factor analysis.

Area of Science:

• Evolutionary biology
• Comparative genomics
• Phylogenetics

Background:

• Biological phenotypes arise from complex evolutionary processes influenced by selective forces.
• Phylogenetic comparative methods are crucial for understanding trait evolution across species.
• Existing methods struggle with high-dimensional data, limiting their application.

Purpose of the Study:

• To develop computationally efficient and flexible phylogenetic factor analysis methods.
• To provide a practical, automated pipeline for complex modeling decisions in phylogenetic analysis.
• To enhance the accessibility and replicability of high-dimensional phylogenetic comparative methods.

Main Methods:

• Developed novel inference techniques for phylogenetic factor analysis.
• Created an automated pipeline to guide researchers through modeling decisions.
• Employed a Bayesian approach to address latent factor uncertainty.

Main Results:

• Achieved significant increases in computational efficiency (up to 500-fold).
• Enhanced model flexibility and reduced the need for extensive parameter tuning.
• Demonstrated utility across diverse datasets including floral traits, domestication, life history, and brain morphology.

Conclusions:

• The new methods and pipeline offer an accessible Bayesian approach to high-dimensional phylogenetic comparative analyses.
• These advancements facilitate broader community adoption of sophisticated evolutionary modeling techniques.
• The approach balances flexibility, speed, and ease of use for large phylogenetic trees.